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因子逻辑
vwap5日变化衰减均值的排名,与开低加权价2日跌幅衰减均值的排名,两者取大后取负;值越大表示vwap上涨和低价下跌两类短期变动的截面排名都越低。
因子公式
(MAX(RANK(DECAYLINEAR(DELTA(VWAP, 5), 3)), RANK(DECAYLINEAR(((DELTA(((OPEN * 0.15) + (LOW *0.85)),2) / ((OPEN * 0.15) + (LOW * 0.85))) * -1), 3))) * -1)BigQuant SQL
SELECT date, instrument, -1*(open*0.15+low*0.85-m_lag(open*0.15+low*0.85, 2))/(open*0.15+low*0.85) AS _front, greatest(pct_rank_by(date, m_decay_linear(amount/volume-m_lag(amount/volume, 5), 3)), pct_rank_by(date, m_decay_linear(_front, 3)))*-1 AS _factor_temp, 1 * _factor_temp AS factorFROM cn_stock_bar1dORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10377_strategy.py --name 'Alpha191 #156 选股' \ --set 'TABLES=["cn_stock_factors_alpha_191"]' --set 'FACTORS=["alpha_156"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-18"' \ && python bqf_10377_strategy.pySELECT date, instrument, alpha_156 FROM cn_stock_factors_alpha_191带入当前因子,在 Cowork 中继续研究。
from bigquant import factor
values = factor.values(["BQF_10377"], "2021-01-01", "2026-09-18")report = factor.analyze( values, start_date="2021-01-01", end_date="2026-09-18", universe=("all", "hs300"), neutralize=(None, "size+industry"), depth="deep",)report.render()